






"""simple docstring"""



import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
						import torch.distributed.checkpoint as dist_cp
						from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
						from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
						from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
						from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


__A						:			List[Any]    							=						get_logger(__name__)



def 		lowercase       (							__snake_case							:					Any						,   __snake_case							:					List[Any]						,   __snake_case							:					Tuple						,   __snake_case							:					Union[str, Any]						,   __snake_case							:					Tuple=0      ):
		os.makedirs(_lowerCamelCase						,   exist_ok=_lowerCamelCase      )
		with FSDP.state_dict_type(
		    _lowerCamelCase						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config      ):
				lowercase_					:					List[str]							=  model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowercase_					:					Optional[Any]							=  F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
						lowercase_					:					Dict							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
						if accelerator.process_index == 0:
								logger.info(F'''Saving model to {output_model_file}'''      )
								torch.save(_lowerCamelCase						,   _lowerCamelCase      )
								logger.info(F'''Model saved to {output_model_file}'''      )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowercase_					:					Any							=  (
						    F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						lowercase_					:					Optional[int]							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
						logger.info(F'''Saving model to {output_model_file}'''      )
						torch.save(_lowerCamelCase						,   _lowerCamelCase      )
						logger.info(F'''Model saved to {output_model_file}'''      )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowercase_					:					List[Any]							=  os.path.join(_lowerCamelCase						,   F'''{MODEL_NAME}_{model_index}'''      )
						os.makedirs(_lowerCamelCase						,   exist_ok=_lowerCamelCase      )
						logger.info(F'''Saving model to {ckpt_dir}'''      )
						lowercase_					:					Union[str, Any]							=  {'''model''': state_dict}

						dist_cp.save_state_dict(
						    state_dict=_lowerCamelCase						,   storage_writer=dist_cp.FileSystemWriter(_lowerCamelCase      )						,   planner=DefaultSavePlanner()						,   )
						logger.info(F'''Model saved to {ckpt_dir}'''      )



def 		lowercase       (							__snake_case							:					str						,   __snake_case							:					Dict						,   __snake_case							:					List[str]						,   __snake_case							:					str						,   __snake_case							:					Tuple=0      ):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    _lowerCamelCase						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config      ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(_lowerCamelCase      ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
										    '''initializing FSDP object'''      )
								return
						lowercase_					:					Any							=  F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
						lowercase_					:					Optional[int]							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
						logger.info(F'''Loading model from {input_model_file}'''      )
						lowercase_					:					str							=  torch.load(_lowerCamelCase      )
						logger.info(F'''Model loaded from {input_model_file}'''      )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowercase_					:					Optional[Any]							=  (
						    F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						lowercase_					:					str							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
						logger.info(F'''Loading model from {input_model_file}'''      )
						lowercase_					:					Optional[int]							=  torch.load(_lowerCamelCase      )
						logger.info(F'''Model loaded from {input_model_file}'''      )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowercase_					:					Optional[Any]							=  (
						    os.path.join(_lowerCamelCase						,   F'''{MODEL_NAME}_{model_index}'''      )
						    if F'''{MODEL_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(F'''Loading model from {ckpt_dir}'''      )
						lowercase_					:					List[str]							=  {'''model''': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=_lowerCamelCase						,   storage_reader=dist_cp.FileSystemReader(_lowerCamelCase      )						,   planner=DefaultLoadPlanner()						,   )
						lowercase_					:					List[str]							=  state_dict['''model''']
						logger.info(F'''Model loaded from {ckpt_dir}'''      )
				model.load_state_dict(_lowerCamelCase      )



def 		lowercase       (							__snake_case							:					Tuple						,   __snake_case							:					Tuple						,   __snake_case							:					List[str]						,   __snake_case							:					int						,   __snake_case							:					Optional[Any]						,   __snake_case							:					Union[str, Any]=0      ):
		os.makedirs(_lowerCamelCase						,   exist_ok=_lowerCamelCase      )
		with FSDP.state_dict_type(
		    _lowerCamelCase						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config      ):
				lowercase_					:					Any							=  FSDP.optim_state_dict(_lowerCamelCase						,   _lowerCamelCase      )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								lowercase_					:					str							=  (
								    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
								)
								lowercase_					:					Tuple							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
								logger.info(F'''Saving Optimizer state to {output_optimizer_file}'''      )
								torch.save(_lowerCamelCase						,   _lowerCamelCase      )
								logger.info(F'''Optimizer state saved in {output_optimizer_file}'''      )
				else:
						lowercase_					:					str							=  os.path.join(_lowerCamelCase						,   F'''{OPTIMIZER_NAME}_{optimizer_index}'''      )
						os.makedirs(_lowerCamelCase						,   exist_ok=_lowerCamelCase      )
						logger.info(F'''Saving Optimizer state to {ckpt_dir}'''      )
						dist_cp.save_state_dict(
						    state_dict={'''optimizer''': optim_state}						,   storage_writer=dist_cp.FileSystemWriter(_lowerCamelCase      )						,   planner=DefaultSavePlanner()						,   )
						logger.info(F'''Optimizer state saved in {ckpt_dir}'''      )






def 		lowercase       (							__snake_case							:					Tuple						,   __snake_case							:					Dict						,   __snake_case							:					List[Any]						,   __snake_case							:					int						,   __snake_case							:					List[Any]						,   __snake_case							:					Dict=0      ):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    _lowerCamelCase						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config      ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowercase_					:					Union[str, Any]							=  None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						lowercase_					:					Tuple							=  (
						    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
						)
						lowercase_					:					Union[str, Any]							=  os.path.join(_lowerCamelCase						,   _lowerCamelCase      )
						logger.info(F'''Loading Optimizer state from {input_optimizer_file}'''      )
						lowercase_					:					Optional[Any]							=  torch.load(_lowerCamelCase      )
						logger.info(F'''Optimizer state loaded from {input_optimizer_file}'''      )
				else:
						lowercase_					:					Any							=  (
						    os.path.join(_lowerCamelCase						,   F'''{OPTIMIZER_NAME}_{optimizer_index}'''      )
						    if F'''{OPTIMIZER_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(F'''Loading Optimizer from {ckpt_dir}'''      )
						lowercase_					:					List[Any]							=  load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()						,   optimizer_key='''optimizer'''						,   storage_reader=dist_cp.FileSystemReader(_lowerCamelCase      )						,   )
						lowercase_					:					Optional[Any]							=  optim_state['''optimizer''']
						logger.info(F'''Optimizer loaded from {ckpt_dir}'''      )
				lowercase_					:					Union[str, Any]							=  FSDP.optim_state_dict_to_load(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
				optimizer.load_state_dict(_lowerCamelCase      )











"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




